Magnetic Resonance Spectroscopy (MRS) is an important non-invasive technique
for in vivo biomedical detection. However, it is still challenging to
accurately quantify metabolites with proton MRS due to three problems: Serious
overlaps of metabolite signals, signal distortions due to non-ideal acquisition
conditions and interference with strong background signals including
macromolecule signals. The most popular software, LCModel, adopts the
non-linear least square to quantify metabolites and addresses these problems by
introducing regularization terms, imperfection factors of non-ideal acquisition
conditions, and designing several empirical priors such as basissets of both
metabolites and macromolecules. However, solving such a large non-linear
quantitative problem is complicated. Moreover, when the signal-to-noise ratio
of an input MRS signal is low, the solution may have a large deviation. In this
work, deep learning is introduced to reduce the complexity of solving this
overall quantitative problem. Deep learning is designed to predict directly the
imperfection factors and the overall signal from macromolecules. Then, the
remaining part of the quantification problem becomes a much simpler effective
fitting and is easily solved by Linear Least Squares (LLS), which greatly
improves the generalization to unseen concentration of metabolites in the
training data. Experimental results show that compared with LCModel, the
proposed method has smaller quantification errors for 700 sets of simulated
test data, and presents more stable quantification results for 20 sets of
healthy in vivo data at a wide range of signal-to-noise ratio. Qnet also
outperforms other deep learning methods in terms of lower quantification error
on most metabolites. Finally, QNet has been deployed on a cloud computing
platform, CloudBrain-MRS, which is open accessed at
this https URL

在此论文中，研究者介绍了一种新方法 QNet，它使用深度学习来准确计量 Magnetic Resonance Spectroscopy (MRS) 中存在挑战的代谢物信号，通过直接预测非完美因素和背景信号来降低最终的计量问题的复杂性，并在大量测试数据和健康体内数据中得到验证和应用。